Jueves 25 de Julio, 2019
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Transdiciplinary research, looking for the "SUMA CAUSAI" (quality life) for every inhabitans in the cities with ICT
We are the research group of cognitive cities created on October 15, 2018 with its acronym CCMA.
Ph.D., Curriculum and Instruction.
Mail: monica.vaca@fci.edu.ec
Doctor in Project and Systems Engineering.
Mail: jaime.meza@fci.edu.ec
Máster Universitario En Dirección e Ingenieria de Sitio Web.
Mail: tatiana.zambrano@fci.edu.ec
Magister En Pedagogia.
Mail: maricela.pinargote@fci.edu.ec
Doctorado en Ciencias y Tecnologias de los Sistemas Complejos.
Mail: leticia.vaca@fci.edu.ec
Magister En Pedagogia.
Mail: lorena.bowen@fci.edu.ec
Doctorado en Ciencias Pedagógicas.
Mail: karina.intriago@fci.edu.ec
Ingeniero en Sistemas.
Mail: ermenson.ordonez@fci.edu.ec
Training academy: Strengthen the skills of civil society and professionals in the statistical and technological field of the country
The objective of the course is to establish the theoretical and general bases of data analysis, visualization and interpretation of data, for this, subjects related to basic statistics, dimensional databases and data processing are taught.
In this course, concepts of machine learning, algorithms and clustering will be applied, for this the Python programming tool will be used.
In this course, knowledge of descriptive and inferential statistics will be developed as the basis for the application of multiple elements that define machine learning techniques.
This course will make it easier to understand fuzzy logic and express more facts using this logic that can be used in many applications, including control systems, expression systems and data mining.
In this course, advanced techniques of statistics will be applied, for machine learning, focused on predictive methods, model creation and the consolidation of knowledge of multivariate statistics.
In this course, definitions of data mining will be imparted, to know positive or negative opinions of people's opinions, as a sentiment analysis technique.
This course will define the application of Deep Learning to real-world scenarios, such as image and video processing, text analysis, natural language processing, recommendation systems and other types of classifiers.
This course plans to cover the fundamental techniques in recommendation systems, from non-personalized and project association recommendations to content-based and collaborative techniques.
We are working to generate linking projects, more projects will come soon.
